Open Access

ARTICLE

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

by Mrityunjoy Gain¹, Md Arifur Rahman¹, Rameswar Debnath¹, Mrim M. Alnfiai², Abdullah Sheikh³, Mehedi Masud³, Anupam Kumar Bairagi^1,*

1 Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
2 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia

* Corresponding Author: Anupam Kumar Bairagi. Email:

Computer Systems Science and Engineering 2023, 46(1), 1059-1077. https://doi.org/10.32604/csse.2023.034809

Received 28 July 2022; Accepted 28 October 2022; Issue published 20 January 2023

Abstract

Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated color components in the training images, the saturated color components have a strong tendency towards desaturated color components in the predicted a * b * channel. To solve the problems, we rebalance the predicted a * b * color channel by smoothing every subregion individually using the average filter. 2 stage k-means clustering technique is applied to divide the subregions. Then we apply Gamma transformation in the entire a * b * channel to saturate the image. We compare our proposed method with several existing methods. From the experimental results, we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to visualize. Additionally, our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Histogram.

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Keywords

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